Search

Now showing items 1-3 of 3

Article

A chickpea genetic variation map based on the sequencing of 3,366 genomes

Varshney, R.K.; Roorkiwal, M.; Shuai Sun; Bajaj, P.; Annapurna Chitikineni; Thudi, M.; Singh, N.P.; Xiao Du; Upadhyaya, H.; Khan, A.W.; Yue Wang; Garg, V.; Guangyi Fan; Cowling, W.A.; Crossa, J.; Gentzbittel, L.; Voss-Fels, K.P.; Valluri, V.K.; Sinha, P.; Singh, V.K.; Ben, C.; Rathore, A.; Punna, R.; Muneendra K. Singh; Tar’an, B.; Chellapilla Bharadwaj; Yasin, M.; Pithia, M.S.; Singh, S.; Soren, K.R.; Kudapa, H.; Jarquín, D.; Cubry, P.; Hickey, L.T.; Dixit, G.P.; Thuillet, A.C.; Hamwieh, A.; Kumar, S.; Deokar, A.; Chaturvedi, S.K.; Francis, A.; Howard, R.; Chattopadhyay, D.; Edwards, D.; Lyons, E.; Vigouroux, Y.; Hayes, B.J.; Von Wettberg, E.; Datta, S.K.; Huanming Yang; Nguyen, H.T.; Jian Wang; Siddique, K.H.M.; Mohapatra, T.; Bennetzen, J.L.; Xun Xu; Xin Liu (Nature Publishing Group, 2021)

Article

Author Correction: A chickpea genetic variation map based on the sequencing of 3,366 genomes (Nature, (2021), 599, 7886, (622-627), 10.1038/s41586-021-04066-1)

Varshney, R.K.; Roorkiwal, M.; Shuai Sun; Bajaj, P.; Annapurna Chitikineni; Thudi, M.; Singh, N.P.; Xiao Du; Upadhyaya, H.D.; Khan, A.; Yue Wang; Garg, V.; Guangyi Fan; Cowling, W.A.; Crossa, J.; Gentzbittel, L.; Voss-Fels, K.P.; Valluri, V.K.; Sinha, P.; Singh, V.K.; Ben, C.; Rathore, A.; Punna, R.; Muneendra K. Singh; Tar’an, B.; Chellapilla Bharadwaj; Yasin, M.; Pithia, M.S.; Singh, S.; Soren, K.R.; Kudapa, H.; Jarquín, D.; Cubry, P.; Hickey, L.T.; Dixit, G.P.; Thuillet, A.C.; Hamwieh, A.; Kumar, S.; Deokar, A.; Chaturvedi, S.K.; Francis, A.; Howard, R.; Chattopadhyay, D.; Edwards, D.; Lyons, E.; Vigouroux, Y.; Hayes, B.J.; Von Wettberg, E.; Datta, S.K.; Huanming Yang; Nguyen, H.T.; Jian Wang; Siddique, K.H.M.; Mohapatra, T.; Bennetzen, J.L.; Xun Xu; Xin Liu (Nature Publishing Group, 2022)

Article

Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea

Roorkiwal, M.; Jarquín, D.; Muneendra K. Singh; Pooran M. Gaur; Chellapilla Bharadwaj; Rathore, A.; Howard, R.; Samineni Srinivasan; Ankit Jain; Garg, V.; Sandip Kale; Annapurna Chitikineni; Shailesh Tripathi; Jones, E.; Robbins, K.; Crossa, J.; Varshney, R.K. (Nature Research; Springer Nature, 2018)

Genomic selection (GS) by selecting lines prior to field phenotyping using genotyping data has the potential to enhance the rate of genetic gains. Genotype × environment (G × E) interaction inclusion in GS models can improve ...

CIMMYT Publications Repository

Seeding innovation ... Nourishing hope

Search

A chickpea genetic variation map based on the sequencing of 3,366 genomes

Author Correction: A chickpea genetic variation map based on the sequencing of 3,366 genomes (Nature, (2021), 599, 7886, (622-627), 10.1038/s41586-021-04066-1)

Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea

Search

Filters

A chickpea genetic variation map based on the sequencing of 3,366 genomes

Author Correction: A chickpea genetic variation map based on the sequencing of 3,366 genomes (Nature, (2021), 599, 7886, (622-627), 10.1038/s41586-021-04066-1)

Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea